# QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection

## TL;DR

This paper introduces QuPWM, a feature extraction method combined with SVM classification for automatic epileptic spike detection in MEG signals, achieving high accuracy and efficiency over a balanced dataset.

## Contribution

The paper presents a novel feature extraction approach using Position Weight Matrix with quantization for MEG spike detection, improving accuracy and reducing feature size.

## Key findings

- Achieved up to 98% accuracy in spike detection.
- Effective feature extraction with PWM improves detection performance.
- Method reduces feature vector size for efficient processing.

## Abstract

Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Localization of the epileptogenic zone is an important step for epileptic patient treatment, which starts with epileptic spike detection. The common practice for spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this paper focuses on using machine learning for automatic detection of epileptic spikes in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features. Second, the extracted features are classified using a Support Vector Machine (SVM) for the purpose of epileptic spikes detection. The proposed technique shows great potential in improving the spike detection accuracy and reducing the feature vector size. Specifically, the proposed technique achieved average accuracy up to 98\% in using 5-folds cross-validation applied to a balanced dataset of 3104 samples. These samples are extracted from 16 subjects where eight are healthy and eight are epileptic subjects using a sliding frame of size of 100 samples-points with a step-size of 2 sample-points